Property Appraisal is an estimate of value based on opinion, of an adequately described property of a specific age, supported by presentation and analysis of appropriate information. In the case of domestic property an inspection is undertaken whereby a series of criteria are assessed. The combined output of the assessed criteria allow the valuer to produce an appraisal or an estimate of value for the property. In addition, a professional valuer may apply his or her own knowledge about the locality to further refine the assessment.
Mass appraisal, as a method of systematic statistical assessment, requires the same criteria as single appraisals. It therefore follows the same principles as outlined above, the main difference between the two forms of assessment being multiplicity. As with single appraisals, mass appraisal models are primarily judged by two core factors of transparency and predictive accuracy. There are various models or hybrid models and associated modelling techniques currently available; many of these however might perform well in either one of the two factors of transparency and accuracy, but not both. This might be explained by issues such as the sample size, its distribution, the stratification used, the homogeneity of the area or other local environmental factors.
It is commonly accepted that location of a property is the most important factor affecting its value. Significant differences in value can occur over short distances, even within a single street. Property appraisers will infer a substantial amount of information about a property from its location, which ability is based largely on local knowledge and experience. In addition, location itself will exert an influence on nearby properties.
Modelling of property values, therefore, should take into account the significant effect of location on property value. However, due to the difficulties of reproducing local knowledge of appraisers in a model, many valuation models do not directly take into account location when valuing properties, instead making use of ‘pseudo-location’ signifiers, such as local amenities, accessibility to services, and the like; however, such signifiers do not directly reflect the influence of location, and so can be inaccurate. In addition, such models typically will incorporate many such pseudo-location signifiers in an attempt to minimise the errors inherent in such an approach; this therefore increases computer processing time, and requires detailed assessment of local areas thereby increasing the expense of generating the models.
Other more complex models do attempt to incorporate location as a factor, all of which require an assessment of neighbourhoods or sub-markets. The housing market is a set of distinct but interrelated sub-markets, encompassing dwellings differentiated by one or several alternative dimensions. However, there is little consensus on whether sub-markets should be defined according to property characteristics, or based on the actual house price.
Sub-markets may be defined in a number of ways. In a spatial context, it is possible to create localised regions formed through the aggregation of units such as postal zones, enumeration districts, or ward boundaries. The use of ‘political’ or other non-property based locational areas creates problems related to boundary positioning; that is, such boundaries have not been drawn up on the basis of property values, and so do not truly reflect the effect of location on property values. Another approach is based on quantitative characteristics of the dwellings, such as house type, size, age, etc.; or house prices may be used to identify sub-markets. These traditional models however assume homogeneity in distribution and thus density over a unit area. Further, no consideration can be given using this type of analytical method to trends which occur across the boundaries of these areal units. Clusters of value which may be higher than average for the whole unit may be ‘lost’ during analysis. Locational analysis is normally encompassed by either assuming that the locational value is constant, or by sub-dividing units into more readily definable areas such as retail, business or financial districts and assuming each to be constant. In many cases the locational value of these districts may be accounted for through the valuers' expert knowledge of the location.
Although these models do attempt to incorporate the effect of location on property values, the sub-markets thereby defined are often unrepresentative of the actual sub-markets, while no attempt is made to treat the effects of location within a sub-market; that is, the models assume that the effect of location within a sub-market is identical on every property within the sub-market. This can lead to inaccurate valuations, and ignores the possibility of locational trends within a sub-market. In order to go some way towards overcoming these difficulties, conventional models can only rely upon creating smaller and smaller sub-markets, using more and more data, which will clearly increase computer processing time and use more computer memory. In addition, the problem of directly considering the effects of location on property values remains unaddressed.
It is among the objects of embodiments of the invention to provide a valuation model whereby the effects of spatial location on property values may be directly incorporated into the model.
It is further among the objects of certain embodiments of the invention to provide a computer-based modelling method and computer program which may generate property valuations at a lower computer processing burden than conventional valuation models, without sacrificing accuracy.